


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 						a__			(				A__,      A__,      A__,      A__,      A__				):
			# load base model
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					StableDiffusionPipeline.from_pretrained(_A,      torch_dtype=torch.floataa				)

			# load LoRA weight from .safetensors
			SCREAMING_SNAKE_CASE_		:     List[str]					=					load_file(_A				)

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[]

			# directly update weight in diffusers model
			for key in state_dict:
						# it is suggested to print out the key, it usually will be something like below
						# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

						# as we have set the alpha beforehand, so just skip
						if ".alpha" in key or key in visited:
									continue

						if "text" in key:
									SCREAMING_SNAKE_CASE_		:     str					=					key.split('.'				)[0].split(LORA_PREFIX_TEXT_ENCODER + '_'				)[-1].split('_'				)
									SCREAMING_SNAKE_CASE_		:     str					=					pipeline.text_encoder
						else:
									SCREAMING_SNAKE_CASE_		:     Any					=					key.split('.'				)[0].split(LORA_PREFIX_UNET + '_'				)[-1].split('_'				)
									SCREAMING_SNAKE_CASE_		:     int					=					pipeline.unet

						# find the target layer
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					layer_infos.pop(0				)
						while len(_A				) > -1:
									try:
												SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					curr_layer.__getattr__(_A				)
												if len(_A				) > 0:
															SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					layer_infos.pop(0				)
												elif len(_A				) == 0:
															break
									except Exception:
												if len(_A				) > 0:
															temp_name += "_" + layer_infos.pop(0				)
												else:
															SCREAMING_SNAKE_CASE_		:     Optional[int]					=					layer_infos.pop(0				)

						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[]
						if "lora_down" in key:
									pair_keys.append(key.replace('lora_down',      'lora_up'				)				)
									pair_keys.append(_A				)
						else:
									pair_keys.append(_A				)
									pair_keys.append(key.replace('lora_up',      'lora_down'				)				)

						# update weight
						if len(state_dict[pair_keys[0]].shape				) == 4:
									SCREAMING_SNAKE_CASE_		:     int					=					state_dict[pair_keys[0]].squeeze(3				).squeeze(2				).to(torch.floataa				)
									SCREAMING_SNAKE_CASE_		:     List[str]					=					state_dict[pair_keys[1]].squeeze(3				).squeeze(2				).to(torch.floataa				)
									curr_layer.weight.data += alpha * torch.mm(_A,      _A				).unsqueeze(2				).unsqueeze(3				)
						else:
									SCREAMING_SNAKE_CASE_		:     str					=					state_dict[pair_keys[0]].to(torch.floataa				)
									SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					state_dict[pair_keys[1]].to(torch.floataa				)
									curr_layer.weight.data += alpha * torch.mm(_A,      _A				)

						# update visited list
						for item in pair_keys:
									visited.append(_A				)

			return pipeline


if __name__ == "__main__":
				lowerCAmelCase__			:      str								=argparse.ArgumentParser()

				parser.add_argument(
				    '--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
				)
				parser.add_argument(
				    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
				)
				parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
				parser.add_argument(
				    '--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
				)
				parser.add_argument(
				    '--lora_prefix_text_encoder',
				    default='lora_te',
				    type=str,
				    help='The prefix of text encoder weight in safetensors',
				)
				parser.add_argument('--alpha', default=0.7_5, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
				parser.add_argument(
				    '--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
				)
				parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')

				lowerCAmelCase__			:      List[Any]								=parser.parse_args()

				lowerCAmelCase__			:      Optional[Any]								=args.base_model_path
				lowerCAmelCase__			:      str								=args.checkpoint_path
				lowerCAmelCase__			:      List[Any]								=args.dump_path
				lowerCAmelCase__			:      str								=args.lora_prefix_unet
				lowerCAmelCase__			:      Dict								=args.lora_prefix_text_encoder
				lowerCAmelCase__			:      Union[str, Any]								=args.alpha

				lowerCAmelCase__			:      int								=convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

				lowerCAmelCase__			:      Optional[Any]								=pipe.to(args.device)
				pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









__UpperCamelCase     		=							2_5_6
# Modulus to hash a string
__UpperCamelCase     		=							1_0_0_0_0_0_3
def 							UpperCamelCase_(					_A :str					,  _A :str	)->		bool:
			UpperCamelCase__       =							len(_A	)
			UpperCamelCase__       =							len(_A	)
			if p_len > t_len:
						return False

			UpperCamelCase__       =							0
			UpperCamelCase__       =							0
			UpperCamelCase__       =							1

			# Calculating the hash of pattern and substring of text
			for i in range(_A	):
						UpperCamelCase__       =							(ord(pattern[i]	) + p_hash * alphabet_size) % modulus
						UpperCamelCase__       =							(ord(text[i]	) + text_hash * alphabet_size) % modulus
						if i == p_len - 1:
									continue
						UpperCamelCase__       =							(modulus_power * alphabet_size) % modulus

			for i in range(0					,  t_len - p_len + 1	):
						if text_hash == p_hash and text[i : i + p_len] == pattern:
									return True
						if i == t_len - p_len:
									continue
						# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
						UpperCamelCase__       =							(
						    (text_hash - ord(text[i]	) * modulus_power) * alphabet_size
						    + ord(text[i + p_len]	)
						) % modulus
			return False
def 							UpperCamelCase_(					)->		None:
			UpperCamelCase__       =							"abc1abc12"
			UpperCamelCase__       =							"alskfjaldsabc1abc1abc12k23adsfabcabc"
			UpperCamelCase__       =							"alskfjaldsk23adsfabcabc"
			assert rabin_karp(_A					,  _A	) and not rabin_karp(_A					,  _A	)

			# Test 2)
			UpperCamelCase__       =							"ABABX"
			UpperCamelCase__       =							"ABABZABABYABABX"
			assert rabin_karp(_A					,  _A	)

			# Test 3)
			UpperCamelCase__       =							"AAAB"
			UpperCamelCase__       =							"ABAAAAAB"
			assert rabin_karp(_A					,  _A	)

			# Test 4)
			UpperCamelCase__       =							"abcdabcy"
			UpperCamelCase__       =							"abcxabcdabxabcdabcdabcy"
			assert rabin_karp(_A					,  _A	)

			# Test 5)
			UpperCamelCase__       =							"Lü"
			UpperCamelCase__       =							"Lüsai"
			assert rabin_karp(_A					,  _A	)
			UpperCamelCase__       =							"Lue"
			assert not rabin_karp(_A					,  _A	)
			print("Success."	)


if __name__ == "__main__":
						test_rabin_karp()


